Ignite Creation Date:
2025-12-26 @ 12:19 PM
Ignite Modification Date:
2025-12-29 @ 4:05 PM
Study NCT ID:
NCT06178900
Status:
COMPLETED
Last Update Posted:
2025-03-24
First Post:
2023-12-14
Is Gene Therapy:
True
Has Adverse Events:
False
Brief Title:
Evaluating AI-Gatekeeper Software in Coronary Artery Stenosis Screening: a Multicenter RCT
Sponsor:
Organization:
Raw JSON
{'hasResults': False, 'derivedSection': {'miscInfoModule': {'versionHolder': '2025-12-24'}, 'conditionBrowseModule': {'meshes': [{'id': 'D003324', 'term': 'Coronary Artery Disease'}, {'id': 'D004194', 'term': 'Disease'}], 'ancestors': [{'id': 'D003327', 'term': 'Coronary Disease'}, {'id': 'D017202', 'term': 'Myocardial Ischemia'}, {'id': 'D006331', 'term': 'Heart Diseases'}, {'id': 'D002318', 'term': 'Cardiovascular Diseases'}, {'id': 'D001161', 'term': 'Arteriosclerosis'}, {'id': 'D001157', 'term': 'Arterial Occlusive Diseases'}, {'id': 'D014652', 'term': 'Vascular Diseases'}, {'id': 'D010335', 'term': 'Pathologic Processes'}, {'id': 'D013568', 'term': 'Pathological Conditions, Signs and Symptoms'}]}}, 'protocolSection': {'designModule': {'phases': ['NA'], 'studyType': 'INTERVENTIONAL', 'designInfo': {'allocation': 'RANDOMIZED', 'maskingInfo': {'masking': 'NONE'}, 'primaryPurpose': 'DIAGNOSTIC', 'interventionModel': 'PARALLEL'}, 'enrollmentInfo': {'type': 'ACTUAL', 'count': 450}}, 'statusModule': {'overallStatus': 'COMPLETED', 'startDateStruct': {'date': '2024-03-01', 'type': 'ACTUAL'}, 'expandedAccessInfo': {'hasExpandedAccess': False}, 'statusVerifiedDate': '2025-03', 'completionDateStruct': {'date': '2025-02-28', 'type': 'ACTUAL'}, 'lastUpdateSubmitDate': '2025-03-20', 'studyFirstSubmitDate': '2023-12-14', 'studyFirstSubmitQcDate': '2023-12-14', 'lastUpdatePostDateStruct': {'date': '2025-03-24', 'type': 'ACTUAL'}, 'studyFirstPostDateStruct': {'date': '2023-12-21', 'type': 'ACTUAL'}, 'primaryCompletionDateStruct': {'date': '2024-09-06', 'type': 'ACTUAL'}}, 'outcomesModule': {'primaryOutcomes': [{'measure': 'MACE (major adverse cardiovascular events)', 'timeFrame': '24 weeks', 'description': 'All-cause death, non-fatal MI, stroke, admission due to acute coronary syndrome'}, {'measure': 'Unnecessary utilization of advanced cardiac imaging', 'timeFrame': '24 weeks', 'description': 'Defined as either (1) confirmation of non-significant coronary artery disease (stenosis ≤50%) by advanced cardiac imaging (CCTA or ICA) or (2) incorrect prediction of significant CAD by the AI-Gatekeeper software.'}], 'secondaryOutcomes': [{'measure': 'Comparison of total healthcare costs', 'timeFrame': '24 weeks', 'description': 'This analysis focuses on comparing the overall financial impact of different healthcare interventions or treatments. It encompasses all associated expenses, from diagnostic procedures to treatment and follow-up care, providing a comprehensive assessment of the economic burden on the healthcare system.'}, {'measure': 'Proportion of subjects classified as positive by the AI-Gatekeeper model analysis who are diagnosed with coronary artery stenosis (≥50%)', 'timeFrame': '24 weeks', 'description': 'This measure assesses the accuracy of the AI-Gatekeeper model in identifying patients with coronary artery stenosis of 50% or greater among those it classifies as positive.'}, {'measure': 'Proportion of subjects identified as negative by the AI-Gatekeeper model who are confirmed to have non-significant stenosis (<50%)', 'timeFrame': '24 weeks', 'description': 'This measure assesses the accuracy of the AI-Gatekeeper model in identifying patients with non-significant coronary artery stenosis (\\<50%) among those it classifies as negative.'}, {'measure': 'Comparison of changes in angina symptom score', 'timeFrame': '24 weeks', 'description': "This comparison evaluates the variation in patients' angina symptoms severity and frequency as measured by the Seattle Angina Questionnaire (SAQ), a standardized tool for assessing chest pain related to heart conditions."}]}, 'oversightModule': {'isUsExport': False, 'oversightHasDmc': False, 'isFdaRegulatedDrug': False, 'isFdaRegulatedDevice': False}, 'conditionsModule': {'keywords': ['Coronary Artery Disease', 'Artificial Intelligence', 'Gatekeeper'], 'conditions': ['Coronary Artery Disease', 'Diagnosis']}, 'referencesModule': {'references': [{'pmid': '34756653', 'type': 'BACKGROUND', 'citation': "Writing Committee Members; Gulati M, Levy PD, Mukherjee D, Amsterdam E, Bhatt DL, Birtcher KK, Blankstein R, Boyd J, Bullock-Palmer RP, Conejo T, Diercks DB, Gentile F, Greenwood JP, Hess EP, Hollenberg SM, Jaber WA, Jneid H, Joglar JA, Morrow DA, O'Connor RE, Ross MA, Shaw LJ. 2021 AHA/ACC/ASE/CHEST/SAEM/SCCT/SCMR Guideline for the Evaluation and Diagnosis of Chest Pain: A Report of the American College of Cardiology/American Heart Association Joint Committee on Clinical Practice Guidelines. J Am Coll Cardiol. 2021 Nov 30;78(22):e187-e285. doi: 10.1016/j.jacc.2021.07.053. Epub 2021 Oct 28."}, {'pmid': '22692650', 'type': 'BACKGROUND', 'citation': 'Genders TS, Steyerberg EW, Hunink MG, Nieman K, Galema TW, Mollet NR, de Feyter PJ, Krestin GP, Alkadhi H, Leschka S, Desbiolles L, Meijs MF, Cramer MJ, Knuuti J, Kajander S, Bogaert J, Goetschalckx K, Cademartiri F, Maffei E, Martini C, Seitun S, Aldrovandi A, Wildermuth S, Stinn B, Fornaro J, Feuchtner G, De Zordo T, Auer T, Plank F, Friedrich G, Pugliese F, Petersen SE, Davies LC, Schoepf UJ, Rowe GW, van Mieghem CA, van Driessche L, Sinitsyn V, Gopalan D, Nikolaou K, Bamberg F, Cury RC, Battle J, Maurovich-Horvat P, Bartykowszki A, Merkely B, Becker D, Hadamitzky M, Hausleiter J, Dewey M, Zimmermann E, Laule M. Prediction model to estimate presence of coronary artery disease: retrospective pooled analysis of existing cohorts. BMJ. 2012 Jun 12;344:e3485. doi: 10.1136/bmj.e3485.'}, {'pmid': '25205628', 'type': 'BACKGROUND', 'citation': 'Renker M, Schoepf UJ, Wang R, Meinel FG, Rier JD, Bayer RR 2nd, Mollmann H, Hamm CW, Steinberg DH, Baumann S. Comparison of diagnostic value of a novel noninvasive coronary computed tomography angiography method versus standard coronary angiography for assessing fractional flow reserve. Am J Cardiol. 2014 Nov 1;114(9):1303-8. doi: 10.1016/j.amjcard.2014.07.064. Epub 2014 Aug 12.'}, {'pmid': '34235441', 'type': 'BACKGROUND', 'citation': 'Kamel PI, Yi PH, Sair HI, Lin CT. Prediction of Coronary Artery Calcium and Cardiovascular Risk on Chest Radiographs Using Deep Learning. Radiol Cardiothorac Imaging. 2021 Jun 17;3(3):e200486. doi: 10.1148/ryct.2021200486. eCollection 2021 Jun.'}, {'pmid': '32200712', 'type': 'BACKGROUND', 'citation': 'Kwon JM, Lee SY, Jeon KH, Lee Y, Kim KH, Park J, Oh BH, Lee MM. Deep Learning-Based Algorithm for Detecting Aortic Stenosis Using Electrocardiography. J Am Heart Assoc. 2020 Apr 7;9(7):e014717. doi: 10.1161/JAHA.119.014717. Epub 2020 Mar 21.'}, {'pmid': '21477786', 'type': 'BACKGROUND', 'citation': 'Min JK, Dunning A, Lin FY, Achenbach S, Al-Mallah MH, Berman DS, Budoff MJ, Cademartiri F, Callister TQ, Chang HJ, Cheng V, Chinnaiyan KM, Chow B, Delago A, Hadamitzky M, Hausleiter J, Karlsberg RP, Kaufmann P, Maffei E, Nasir K, Pencina MJ, Raff GL, Shaw LJ, Villines TC. Rationale and design of the CONFIRM (COronary CT Angiography EvaluatioN For Clinical Outcomes: An InteRnational Multicenter) Registry. J Cardiovasc Comput Tomogr. 2011 Mar-Apr;5(2):84-92. doi: 10.1016/j.jcct.2011.01.007. Epub 2011 Feb 1.'}, {'pmid': '35928935', 'type': 'BACKGROUND', 'citation': 'Kim J, Lee SY, Cha BH, Lee W, Ryu J, Chung YH, Kim D, Lim SH, Kang TS, Park BE, Lee MY, Cho S. Machine learning models of clinically relevant biomarkers for the prediction of stable obstructive coronary artery disease. Front Cardiovasc Med. 2022 Jul 19;9:933803. doi: 10.3389/fcvm.2022.933803. eCollection 2022.'}]}, 'descriptionModule': {'briefSummary': 'The purpose of this study is to determine the efficacy, safety, and cost-effectiveness of AI-Gatekeeper software to assist clinicians in the diagnosis of coronary artery disease by predicting coronary artery stenosis (≥50%) from a multimodal AI technology that integrates clinical risk factors and baseline blood tests, including chest X-ray, electrocardiogram, and echocardiogram, in patients with suspected coronary artery disease (coronary stenosis).', 'detailedDescription': "Coronary artery disease (CAD) is a leading cause of global mortality, accounting for over 50% of heart disease-related deaths. Initial evaluations for CAD typically involve chest X-rays, electrocardiograms (ECG), risk factor assessments, and basic blood tests. However, these primary tests can't conclusively diagnose CAD. When CAD is suspected, coronary CTA (CCTA) or invasive coronary angiography (ICA) is performed, determining the need for procedures like stenting or revascularization.\n\nInterestingly, over 50% of patients undergoing CCTA or ICA don't require treatment, as CAD is either absent or not severe enough. This leads to unnecessary procedures and significant healthcare costs. For instance, in the U.S., the cost of unnecessary ICAs reaches billions annually, with similar trends in South Korea.\n\nAI-Gatekeeper software assists clinicians in diagnosing coronary artery disease by predicting coronary artery stenosis (≥50%) using multimodal AI technology. It integrates clinical risk factors and baseline blood tests, including chest X-ray, electrocardiogram, and echocardiogram, in patients with suspected coronary artery disease The purpose of this study is to determine the efficacy, safety, and cost-effectiveness of the AI-Gatekeeper software in a prospective, multicenter, randomized control trial."}, 'eligibilityModule': {'sex': 'ALL', 'stdAges': ['ADULT', 'OLDER_ADULT'], 'maximumAge': '80 Years', 'minimumAge': '19 Years', 'healthyVolunteers': False, 'eligibilityCriteria': 'Inclusion Criteria:\n\n* A patient with symptoms such as chest pain suggestive of coronary artery disease, who underwent routine evaluations including blood tests, electrocardiogram, chest X-ray, and echocardiography\n* Low to Intermediate risk of pretest probabilities of obstructive CAD\n* Voluntarily agreed to participate in this clinical trial and signed the written consent form\n\nExclusion Criteria:\n\n* Acute chest pain (in patients who have not been ruled out for ACS)\n* Previously diagnosed and treated coronary artery disease (myocardial infarction, PCI, CABG)\n* Patients with a life expectancy of less than 2 years due to conditions other than heart disease\n* Those who have not consented to the protocol\n* Participated in a drug or medical device clinical trial within the last 3 months\n* Pregnant or lactating women\n* Allergic to iodine preparations\n* Serum creatine level greater than 1.5 mg/dL or eGFR less than 30 mL/min\n* Baseline irregular and uncontrolled heart rhythm\n* Heart rate greater than 100 beats/minute\n* Systolic blood pressure of 90 mm Hg or less\n* Contraindications to beta blockers or nitroglycerin\n* Patients with complex congenital heart disease\n* Body mass index greater than or equal to 35'}, 'identificationModule': {'nctId': 'NCT06178900', 'acronym': 'AIGatekeeper', 'briefTitle': 'Evaluating AI-Gatekeeper Software in Coronary Artery Stenosis Screening: a Multicenter RCT', 'organization': {'class': 'INDUSTRY', 'fullName': 'INFINITT Healthcare'}, 'officialTitle': 'A Prospective, Multicenter, Open-label, Randomized, Comparative Clinical Trial to Verify the Effectiveness, Safety, and Cost-effectiveness of AI-Gatekeeper, a Multimodal AI Software, in Assisting the Screening of Coronary Artery Stenosis', 'orgStudyIdInfo': {'id': 'AI-Gatekeeper Pro'}}, 'armsInterventionsModule': {'armGroups': [{'type': 'EXPERIMENTAL', 'label': 'Assisted by the AI-Gatekeeper software group', 'description': 'After a baseline examination (chest X-ray, electrocardiogram, echocardiogram, clinical risk factors and blood test), the AI-Gatekeeper software will be used to guide clinical care.', 'interventionNames': ['Diagnostic Test: Assisted by the AI-Gatekeeper software group']}, {'type': 'NO_INTERVENTION', 'label': 'Usual care group', 'description': 'The usual care group will be managed based on established guidelines.'}], 'interventions': [{'name': 'Assisted by the AI-Gatekeeper software group', 'type': 'DIAGNOSTIC_TEST', 'description': 'The group will be received a AI-Gatekeeper software report on the probability of having coronary artery stenosis (≥50%) based on the routine test.', 'armGroupLabels': ['Assisted by the AI-Gatekeeper software group']}]}, 'contactsLocationsModule': {'locations': [{'zip': '16995', 'city': 'Bucheon-si', 'state': 'Gyeonggi-do', 'country': 'South Korea', 'facility': 'Soonchunhyang University Bucheon Hospital', 'geoPoint': {'lat': 37.49889, 'lon': 126.78306}}, {'zip': '16995', 'city': 'Seongnam-si', 'state': 'Gyeonggi-do', 'country': 'South Korea', 'facility': 'Seoul National University Bundang Hospital', 'geoPoint': {'lat': 37.43861, 'lon': 127.13778}}, {'zip': '16995', 'city': 'Yongin', 'state': 'Gyeonggi-do', 'country': 'South Korea', 'facility': 'Yongin Severance Hospitall, Yonsei University College of Medicine'}, {'city': 'Incheon', 'country': 'South Korea', 'facility': "Catholic Kwandong University International St. Mary's Hospital", 'geoPoint': {'lat': 37.45646, 'lon': 126.70515}}, {'city': 'Seoul', 'country': 'South Korea', 'facility': 'Hanyang University Seoul Hospital', 'geoPoint': {'lat': 37.566, 'lon': 126.9784}}], 'overallOfficials': [{'name': 'In Hyun Jung, MD, PhD', 'role': 'PRINCIPAL_INVESTIGATOR', 'affiliation': 'Yongin Severance Hospital, Yonsei University College of Medicine'}]}, 'ipdSharingStatementModule': {'ipdSharing': 'NO'}, 'sponsorCollaboratorsModule': {'leadSponsor': {'name': 'INFINITT Healthcare', 'class': 'INDUSTRY'}, 'collaborators': [{'name': 'Korea Medical Device Development Fund', 'class': 'UNKNOWN'}], 'responsibleParty': {'type': 'SPONSOR'}}}}